Previous work demonstrated that multi-task learning techniques (MTL) could be used to jointly model
both gender and emotion [1]}, [2]}, [3]}, resulting in consistent performance increases compared to
gender-agnostic models. Previous work also showed that emotion recognition systems could be improved by incorporating speaker identity as a feature, along with the other emotion-related features [4]}. It has also been demonstrated that some features (e.g., pitch) are not only valuable for predicting emotions, but are also effective for detecting weight, height, gender, etc. [5]}.
Zhang et al. [6]} explored MTL frameworks for leveraging data from different domains (speech and song) and gender in emotion recognition systems. Xia et al. [7]} treated dimensional and categorical labels as two different tasks.
